Objective
Cheaper and more powerful sensors, together with big data analytics, offer an unprecedented opportunity to track machine-tool performance and health condition. However, manufacturers only spend 15% of their total maintenance costs on predictive (vs reactive or preventative) maintenance.
The project will deploy and test a predictive cognitive maintenance decision-support system able to identify and localize damage, assess damage severity, predict damage evolution, assess remaining asset life, reduce the probability of false alarms, provide more accurate failure detection, issue notices to conduct preventive maintenance actions and ultimately increase in-service efficiency of machines by at least 10%.
The platform includes 4 modules: 1) a data acquisition module leveraging external sensors as well as sensors directly embedded in the machine tool components, 2) an artificial intelligence module combining physical models, statistical models and machine-learning algorithms able to track individual health condition and supporting a large range of assets and dynamic operating conditions, 3) a secure integration module connecting the platform to production planning and maintenance systems via a private cloud and providing additional safety, self-healing and self-learning capabilities and 4) a human interface module including production dashboards and augmented reality interfaces for facilitating maintenance tasks.
The consortium includes 3 end-user factories, 3 machine-tool suppliers, 1 leading component supplier, 4 innovative SMEs, 3 research organizations and 3 academic institutions. Together, we will validate the platform in a broad spectrum of real-life industrial scenarios (low volume, high volume and continuous manufacturing). We will also demonstrate the direct impact of the platform on maintainability, availability, work safety and costs in order to document the results in detailed business cases for widespread industry dissemination and exploitation.
Fields of science
- natural sciencescomputer and information sciencesdata sciencebig data
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
- social scienceseconomics and businesseconomicsproduction economics
- natural sciencesmathematicsapplied mathematicsstatistics and probability
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Programme(s)
Funding Scheme
IA - Innovation action
Coordinator
35195 Vaxjo
Sweden
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Participants (16)
351 96 Vaxjo
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
11146 Galatsi N Attikis
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
20018 Donostia
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
06000 Nice
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
97816 Lohr
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20570 Bergara
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31830 Lakuntza
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35745 Herbon
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080 01 Haniska
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43450 La Riba Tarragona
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20400 Tolosa
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
20870 Elgoibar
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75015 Paris 15
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15782 Santiago De Compostela
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80333 Muenchen
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09111 Chemnitz
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